FinalProjectDraft
Breanna Schneider, Avery Chan, Maja Velichkovich
4/11/2021
1 Introduction
Spotify is one of the larger music streaming services available today with 345 million active users1. Instead of having to buy cds or download every song to listen to, Spotify allows access to millions of songs without having to download them on electronic devices.
In our project, we want to answer if energy, acousticness, loudness, danceability, and liveness have a specific pattern relating to the genres. In addition, our other question is if a feature has a strong correlation to certain other features. Certain features will have strong patterns relating to the year and some of the features will be strongly correlated to other features. We especially think that energy and danceability will have a strong correlation, along with liveness and energy.
2 Background
The data we are using is based on Spotify data from 1921 to 2020 including over 175,000 audio tracks. We found our data on Kaggle2. This dataset groups the data by artist, genre, and year. There are nine different variables measured in the dataset. They are acousticness, danceability, duration, energy, liveness, instrumentalness, loudness, speechiness and tempo.
For our project, we decided to focus on energy, acousticness, liveness, loudness and danceability. Energy is a perceptual measure of the intensity and activity of a track on a scale from 0.0 to 1.0. Some of the perceptual features that are included in this are dynamic range, perceived loudness, timbre, onset rate, and general entropy. Liveness ranges from 0 to 1 and detects if an audience is present in a recording. If the liveness value is above 0.8, there is a strong likelihood that the track is live. Acousticness is the confidence measure of the track being acoustic. It varies from 0.0 to 1.0, with 1.0 representing high confidence that the track is acoustic. Loudness ranges from -60 to 0 and is measured in decibels (dB). It suggests the overall loudless averaged over the entire track. Lastly, the measure of danceability includes a combination of tempo, rhythm stability, beat strength and regularity. It rates how suitable a track is for dancing from 0.0 to 1.0 with 1 being the most danceable.
In the rest of our report, we intend to first graph each feature by genre and add a linear regression line to see if there are any trends over the years. Then, we will test the correlations between two features to see if they are strongly related or not related. In the end, we hope to discover how different features have changed over the years and how music has evolved.
3 Analysis
3.1 Genre Condensation
There were 3232 genres. We condensed these into the top 20 occurring terms in these genres using regular expressions and counting the occurrences.
3.1.1 All genres
Could include all genres here just to show
3.1.2 Top 100
Could show all generes and counts here (or like 100) just to show
3.1.3 Top 20
3.1.4 Final Condensed Gernre Dataset
We use these top 20 to create a more concisely labeled dataset (along with the label other).
3.1.5 Condensed genre conunts
3.1.6 Condensed genre conunts (removed “other”)
3.2 r-values
3.2.1 All combinations
3.2.1.1 All Combinations
The second question we want to answer is to see if any features have strong linear correlations to other features. First, we found r-value combinations between all of the features, which is shown in the table below.
3.2.1.1.1 A
3.2.1.1.2 B
3.2.1.2 Filter r values
As this table shows, some features seem to have strong linear relationships, while some features seem to not have a strong linear relationship. Next, we filtered for the absolute value of r-values only over .9 to find the strongest feature relations.
3.2.1.3 Filtered Graphed
Energy is included in each of the three strongest correlations. Energy is strongly related to acousticness, loudness and tempo. The three plots show each of the three features graphed vs energy. Each graph includes a line with a linear method to show the approximate linear regression line. While an increase in acousticness relates to a decrease in energy, an increase in loudness and tempo relates to an increase in energy.
3.2.1.4 ???
3.3 Genre Graphs
3.3.1 Explaination
WOrds
3.3.2 Boxplots
3.3.2.1 Genres vs acousticness (geom_boxplot)
3.3.2.2 Genres vs danceability (geom_boxplot)
3.3.2.3 Genres vs duration_ms (geom_boxplot)
3.3.2.4 Genres vs energy (geom_boxplot)
3.3.2.5 Genres vs instrumentalness (geom_boxplot)
3.3.2.6 Genres vs liveness (geom_boxplot)
3.3.2.7 Genres vs loudness (geom_boxplot)
3.3.2.8 Genres vs speechiness (geom_boxplot)
3.3.2.9 Genres vs tempo (geom_boxplot)
3.3.2.10 Genres vs valence (geom_boxplot)
3.3.2.11 Genres vs popularity (geom_boxplot)
3.3.2.12 Genres vs tempo (geom_boxplot)
3.3.2.13 Genres vs popularity (geom_boxplot)
3.3.3 Density
3.3.4 Popularity
4 Discussion
We also discovered which features have the strongest linear correlations to each other, vs which features have no linear relationship. We found that energy has a correlation over the absolute value of 0.9 to three other features, acousticness, loudness and tempo. Acousticness has a negative correlation with energy while loudness and tempo both have positive correlations with energy. Considering that acousticness, loudness, and tempo are all measured based on set measurements, while energy is calculated from intensity and activity in the song, we can infer that acousticness, loudness, and tempo all affect the energy of a song.
A short-coming of our analysis is that we do not know how many songs are included in the data for each year. Some year’s data may be based on more songs than other years.
Future work on this dataset could involve testing out more of the features relationships and seeing if they have strong models. We could also look for datasets from other music streaming services, such as Apple Music and Pandora.